Feature Inference Attack on Model Predictions in Vertical Federated Learning

Federated learning (FL) is an emerging paradigm for facilitating multiple organizations' data collaboration without revealing their private data to each other. Recently, vertical FL, where the participating organizations hold the same set of samples but with disjoint features and only one organization owns the labels, has received increased attention. This paper presents several feature inference attack methods to investigate the potential privacy leakages in the model prediction stage of vertical FL. The attack methods consider the most stringent setting that the adversary controls only the trained vertical FL model and the model predictions, relying on no background information. We first propose two specific attacks on the logistic regression (LR) and decision tree (DT) models, according to individual prediction output. We further design a general attack method based on multiple prediction outputs accumulated by the adversary to handle complex models, such as neural networks (NN) and random forest (RF) models. Experimental evaluations demonstrate the effectiveness of the proposed attacks and highlight the need for designing private mechanisms to protect the prediction outputs in vertical FL.Comment: 12 page

A Fusion-Denoising Attack on InstaHide with Data Augmentation

InstaHide is a state-of-the-art mechanism for protecting private training images, by mixing multiple private images and modifying them such that their visual features are indistinguishable to the naked eye. In recent work, however, Carlini et al. show that it is possible to reconstruct private images from the encrypted dataset generated by InstaHide. Nevertheless, we demonstrate that Carlini et al.'s attack can be easily defeated by incorporating data augmentation into InstaHide. This leads to a natural question: is InstaHide with data augmentation secure? In this paper, we provide a negative answer to this question, by devising an attack for recovering private images from the outputs of InstaHide even when data augmentation is present. The basic idea is to use a comparative network to identify encrypted images that are likely to correspond to the same private image, and then employ a fusion-denoising network for restoring the private image from the encrypted ones, taking into account the effects of data augmentation. Extensive experiments demonstrate the effectiveness of the proposed attack in comparison to Carlini et al.'s attack.Comment: 15 page

Numerical Study on Effects of the Embedded Monopile Foundation on Local Wave-Induced Porous Seabed Response

Effects of the embedded monopile foundation on the local distributions of pore water pressure, soil stresses, and liquefaction are investigated in this study using a three-dimensional integrated numerical model. The model is based on a Reynolds-Averaged Navier-Stokes wave module and a fully dynamic poroelastic seabed module and has been validated with the analytical solution and experimental data. Results show that, compared to the situation without an embedded foundation, the embedded monopile foundation increases and decreases the maximum pore water pressure in the seabed around and below the foundation, respectively. The embedded monopile foundation also significantly modifies the distributions of the maximum effective soil stress around the foundation and causes a local concentration of soil stress below the two lower corners of foundation. A parametric study reveals that the effects of embedded monopile foundation on pore water pressure increase as the degrees of saturation and soil permeability decrease. The embedded monopile foundation tends to decrease the liquefaction depth around the structure, and this effect is relatively more obvious for greater degrees of saturation, greater soil permeabilities, and smaller wave heights

ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models

Colonoscopy analysis, particularly automatic polyp segmentation and detection, is essential for assisting clinical diagnosis and treatment. However, as medical image annotation is labour- and resource-intensive, the scarcity of annotated data limits the effectiveness and generalization of existing methods. Although recent research has focused on data generation and augmentation to address this issue, the quality of the generated data remains a challenge, which limits the contribution to the performance of subsequent tasks. Inspired by the superiority of diffusion models in fitting data distributions and generating high-quality data, in this paper, we propose an Adaptive Refinement Semantic Diffusion Model (ArSDM) to generate colonoscopy images that benefit the downstream tasks. Specifically, ArSDM utilizes the ground-truth segmentation mask as a prior condition during training and adjusts the diffusion loss for each input according to the polyp/background size ratio. Furthermore, ArSDM incorporates a pre-trained segmentation model to refine the training process by reducing the difference between the ground-truth mask and the prediction mask. Extensive experiments on segmentation and detection tasks demonstrate the generated data by ArSDM could significantly boost the performance of baseline methods.Comment: Accepted by MICCAI-202

A model to predict the thermodynamic stability of abiotic methane-hydrogen binary hydrates in a marine serpentinization environment

Abiotic methane (CH4) and hydrogen (H2), which are produced during marine serpentinization, provide abundant gas source for hydrate formation on ocean floor. However, previous models of CH4–H2 hydrate formation have generally focused on pure water environments and have not considered the effects of salinity. In this study, the van der Waals–Platteeuw model, which considered the effects of salinity on the chemical potentials of CH4, H2, and H2O, was applied in a marine serpentinization environment. The model uses an empirical formula and the Peng–Robinson equation of state to calculate the Langmuir constants and fugacity values, respectively, of CH4 and H2, and it uses the Pitzer model to calculate the activity coefficients of H2O in the CH4–H2–seawater system. The three-phase equilibrium temperature and pressure predicted by the model for CH4–H2 hydrates in pure water demonstrated good agreement with experimental data. The model was then used to predict the three-phase equilibrium temperature and pressure for CH4–H2 hydrates in a NaCl solutions, for which relevant experimental data are lacking. Thus, this study provides a theoretical basis for gas hydrate research and investigation in areas with marine serpentinization

Research on CVDs Prediction and Early Warning Techniques in Healthcare Monitoring System

Abstract-Chronic diseases are gradually becoming the principal factors of harm to people's health. Fortunately, the development of e-health provides a novel thought for chronic disease prevention and treatment. This paper focuses on the research of cardiovascular disease (CVDs) prevention and early warning techniques using e-health and data mining. In this paper, we will use weighted associative classification algorithm to model the data in healthcare database to determine the level of cardiovascular risk. Besides, on the basis of data mining and knowledge discovery, intelligent warning mechanisms are proposed to provide different services to patients with different levels of risk. The experimental results show that the used classification algorithm is a more effective mining algorithm in the field of healthcare with higher accuracy and better comprehension. Our study is of definite significance to help control risk level of CVDs patients